The Internet has already been popularized as the important media along with the publishing, broadcasting, and the like. Particularly in recent years, websites called SNS (Social Network Service), specifically Twitter, Facebook, Mixi (all registered trademarks), and the like are dramatically popularized and have come to impose great influence upon the society.
When negative rumors for a specific business corporation or product is written in SNS, web blog, or the like on a website, the rumors rapidly spread and cause critical bad influence for the sales thereof, i.e., generates damages by rumors. Thus, the business corporations cannot disregard the state of the rumors on SNS and the like in terms of the crisis management.
Therefore, there already exist a great number of techniques and services for analyzing the contribution state on SNS as disclosed in Non-Patent Document 1, for example. Further, as in Patent Document 1, there is already known a technique which estimates the influence imposed upon other media based on theories such as the mechanical learning and the mathematical statistics by each kind of websites, and predicts the future contribution state based on that information.
Patent Document 1: WO 2009/116342
Non-Patent Document 1: “Branding Analysis (Grasp and Measure for Damages caused by Rumors)”. IBM Japan Searched on Nov. 4, 2011, Internet <URL: http://www-06.ibm.com/services/bcs/jp/solutions/sc/pdf/branding.pdf>
However, those techniques predict the future contribution state by calculating the influence by “each kind of websites” but do not estimate it by “each contributor (or attribute of contributor)”. Thus, there is no such specific technique which can accurately predict the future contribution state, so that the only way is to take a measure by predicting the future contribution state based on the contribution state of the past to the present based on the human intuition.
In addition to that, there are many cases where all the contributions from the past to the present cannot be acquired with the websites such as SNS due to the restrictions of the system and managerial controls. With Twitter, for example, only 10% of all the contributions can be acquired even if permission for the use thereof can be acquired from the operating company. It is only 1% that can be acquired without the permission for the use. Moreover, a specific topic that has not been the subject of talks in the society may become a target of monitoring because of occurrence of some kind of events. In the cases corresponding to each of the above-described circumstances, amount of the contribution data that can be used for estimation of the influence is small.
Further, even when all the contribution data can be acquired, the number of contributions and the contributors is extremely large. Thus, it often happens that all the acquired data cannot be utilized due to the restrictions such as the processing capacity of the computer to be used for the processing. Because of the above-described reasons, it is difficult to estimate the influence of each contributor. It is even more difficult to predict the future contribution state based thereupon.
It is therefore an object of the present invention to provide an information spread scale prediction device, an information spread scale prediction method, and an information spread scale prediction program, which make it possible to accurately predict the influence of each contributor and the number of future contributions for a specific topic in websites such as SNS.
In order to achieve the foregoing object, the information spread scale prediction device according to the present invention is an information spread scale prediction device which acquires text data from a specific website via the Internet, predicts number of future contributions made to the website based on the text data, and outputs a prediction result thereof, and the information spread scale prediction device is characterized to include: a learning text data input unit which acquires the text data from the specific website as learning text data; a node influence learning unit which classifies the learning text data by each topic, calculates influence for the number of contributions by each group to which a node indicating a specific user belongs for the topic from the number of contributions of each of the classified topics, and stores a result thereof as learning data to a storage module provided in advance; a prediction text data input unit which acquires the text data from the specific website as prediction text data after storing the learning data; and a future contribution number prediction unit which classifies the prediction text data by each topic, predicts the number of contributions of the topics at a specific future time based on the number of contributions by each of the classified topics and the learning data, and outputs a result thereof to an output module provided in advance.
In order to achieve the foregoing object, the information spread scale prediction method according to the present invention is used with an information spread scale prediction device which acquires text data from a specific website via the Internet, predicts number of future contributions made to the website based on the text data, and outputs a prediction result thereof, and the method is characterized that: a learning text data input unit acquires the text data from the specific website as learning text data; a node influence learning unit classifies the learning text data by each topic; the node influence learning unit calculates influence for the number of contributions by each group to which a node indicating a specific user belongs for the topic from the number of contributions of each of the classified topics, and stores a result thereof as learning data to a storage module provided in advance; a prediction text data input unit acquires the text data from the specific website as prediction text data after storing the learning data; a future contribution number prediction unit classifies the prediction text data by each topic, the future contribution number prediction unit predicts the number of contributions of the topics at a specific future time based on the number of contributions by each of the classified topics and the learning data; and the future contribution number prediction unit outputs a result thereof to an output module provided in advance.
In order to achieve the foregoing object, the information spread scale prediction program according to the present invention is used in an information spread scale prediction device which acquires text data from a specific website via the Internet, predicts number of future contributions made to the website based on the text data, and outputs a prediction result thereof, and the program is characterized to cause a computer provided to the information spread scale prediction device to execute: a procedure for acquiring the text data from the specific website as learning text data; a procedure for classifying the learning text data by each topic; a procedure for calculating influence for the number of contributions by each group to which a node indicating a specific user belongs for the topic from the number of contributions of each of the classified topics, and storing a result thereof as learning data to a storage module provided in advance; a procedure for acquiring the text data from the specific website as prediction text data after storing the learning data; a procedure for classifying the prediction text data by each topic; a procedure for predicting the number of contributions of the topics at a specific future time based on the number of contributions by each of the classified topics and the learning data; and a procedure for outputting a result thereof to an output module provided in advance.
As described above, the present invention is structured to calculate the influence of a specific user for a specific topic from learning text data acquired from a specific website, save it as learning data, and predict the number of future contributions for the specific topic from the learning data and the prediction text data acquired additionally. Therefore, it is possible to execute the processing for prediction with an amount data that can be practically calculated.
This makes it possible to provide the information spread scale prediction device, the information spread scale prediction method, and the information spread scale prediction program, which exhibit such an excellent feature of making it possible to accurately predict the influence of each contributor and the number of future contributions for a specific topic in websites such as SNS.